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Conference Paper: Multi-objective Optimization Using BFO Algorithm

TitleMulti-objective Optimization Using BFO Algorithm
Authors
Niu, BWang, HTan, LJXu, J
KeywordsBacterial foraging algorithm
multi-objective optimization
pareto optimal
Issue Date2012
PublisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/
Citation
The 7th International Conference on Intelligent Computing (ICIC), Zhengzhou, China, 11-14 August 2011. In Lecture Notes in Computer Science, 2012, v. 6840, p. 582-587 How to Cite?
DOI: http://dx.doi.org/10.1007/978-3-642-24553-4_77
AbstractThis paper describes a novel bacterial foraging optimization (BFO) approach to multi-objective optimization, called Multi-objective Bacterial Foraging Optimization (MBFO). The search for Pareto optimal set of multi-objective optimization problems is implemented. Compared with the proposed algorithm MOPSO and NSGAII, simulation results (measured by Diversity and Generational Distance metric) on test problems show that the proposed MBFO is able to find a much better spread of solutions and faster convergence to the true Pareto-optimal front. It suggests that the proposed MBFO is very promising in dealing with multi-objective optimization problems.
DescriptionLecture Notes in Computer Science vol. 6840 entitled: Bio-inspired computing and applications: 7th International Conference on Intelligent Computing, ICIC 2011, Zhengzhou, China, August 11-14 2011: revised selected papers
Persistent Identifierhttp://hdl.handle.net/10722/198948
ISBN
9783642245527
ISSN
0302-9743
2020 SCImago Journal Rankings: 0.249

 

DC FieldValueLanguage
dc.contributor.authorNiu, Ben_US
dc.contributor.authorWang, Hen_US
dc.contributor.authorTan, LJen_US
dc.contributor.authorXu, Jen_US
dc.date.accessioned2014-07-22T00:56:46Z-
dc.date.available2014-07-22T00:56:46Z-
dc.date.issued2012en_US
dc.identifier.citationThe 7th International Conference on Intelligent Computing (ICIC), Zhengzhou, China, 11-14 August 2011. In Lecture Notes in Computer Science, 2012, v. 6840, p. 582-587en_US
dc.identifier.isbn9783642245527-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10722/198948-
dc.descriptionLecture Notes in Computer Science vol. 6840 entitled: Bio-inspired computing and applications: 7th International Conference on Intelligent Computing, ICIC 2011, Zhengzhou, China, August 11-14 2011: revised selected papers-
dc.description.abstractThis paper describes a novel bacterial foraging optimization (BFO) approach to multi-objective optimization, called Multi-objective Bacterial Foraging Optimization (MBFO). The search for Pareto optimal set of multi-objective optimization problems is implemented. Compared with the proposed algorithm MOPSO and NSGAII, simulation results (measured by Diversity and Generational Distance metric) on test problems show that the proposed MBFO is able to find a much better spread of solutions and faster convergence to the true Pareto-optimal front. It suggests that the proposed MBFO is very promising in dealing with multi-objective optimization problems.en_US
dc.languageengen_US
dc.publisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/en_US
dc.relation.ispartofLecture Notes in Computer Science-
dc.rightsThe original publication is available at www.springerlink.com-
dc.subjectBacterial foraging algorithm-
dc.subjectmulti-objective optimization-
dc.subjectpareto optimal-
dc.titleMulti-objective Optimization Using BFO Algorithmen_US
dc.typeConference_Paperen_US
dc.identifier.emailXu, J: frankxu@hkucc.hku.hken_US
dc.identifier.doi10.1007/978-3-642-24553-4_77en_US
dc.identifier.scopuseid_2-s2.0-84862946902-
dc.identifier.hkuros231530en_US
dc.identifier.volume6840en_US
dc.identifier.spage582en_US
dc.identifier.epage587en_US
dc.publisher.placeGermany-
dc.identifier.issnl0302-9743-

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